Introduction to Information Retrieval
Introduction to Information Retrieval
Knowledge derived from wikipedia for computing semantic relatedness
Journal of Artificial Intelligence Research
RelFinder: Revealing Relationships in RDF Knowledge Bases
SAMT '09 Proceedings of the 4th International Conference on Semantic and Digital Media Technologies: Semantic Multimedia
DBpedia: a nucleus for a web of open data
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
dbrec: music recommendations using DBpedia
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Picasso - to sing, you must close your eyes and draw
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Foxtrot: a soundtrack for where you are
Proceedings of Interacting with Sound Workshop: Exploring Context-Aware, Local and Social Audio Applications
Location-adapted music recommendation using tags
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
A generic semantic-based framework for cross-domain recommendation
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Proceedings of the 20th ACM international conference on Multimedia
Composite interests' exploration thanks to on-the-fly linked data spreading activation
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Discovery hub: on-the-fly linked data exploratory search
Proceedings of the 9th International Conference on Semantic Systems
Location-aware music recommendation using auto-tagging and hybrid matching
Proceedings of the 7th ACM conference on Recommender systems
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In this paper we address a particular recommendation task: retrieving musicians suited for a place of interest (POI). We present a knowledge-based framework built upon the DBpedia ontology linking items from different domains. Graph-based algorithms are used for ranking and filtering items in a target domain (music) with respect to their relatedness to an input item in a source domain (POIs). By conducting user studies we found that users appreciate and judge more valuable the suggestions generated by the proposed approach when a novel weight spreading activation algorithm is used to compute the matching between musicians and POIs. Moreover, users perceive compositions of the suggested musicians as suited for the POIs.